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On September 28, 2010, Ethan Basch made this presentation at the 2010 Annual Conference. Select to access the PowerPoint® presentation (3 MB).

Slide 1

AHRQ Annual Conference

Patient-Reported Outcomes for Adverse Event Monitoring in Clinical Research

Ethan Basch, M.D., M.Sc.
Memorial Sloan-Kettering Cancer Center

September 28, 2010

No Financial Disclosures

Slide 2

Adverse Event Monitoring

Essential activity in Clinical Research:

To ensure patient safety.

To provide data about drug effects:

Trialists, regulators, payors, clinicians, patients

Core activity in routine care:

To guide therapy and supportive care

Slide 3

Data Sources Differ By Type of AE

Category

Example

Data Source

Laboratory value

Anemia

Lab report

Clinical observation/measurement

Retinal tear

Clinical staff

Symptom

Nausea

Clinical staff vs. patients

Slide 4

Clinicians systematically downgrade symptoms compared with patients

Image: Six line graphs show Patient-reported vs. Clinician-reported is shown for 6 conditions: fatigue, anorexia, nausea, vomiting, diarrhea, and constipation. For each of condition, patients report more symptoms than clinicians.

Slide 8

Image: The "Patient Experiences Symptom to Research Database" is shown. A text box labeled "Patient experiences symptoms" is in the upper-left corner of the slide, and an arrow points down to a data bin labeled "Research database" in the lower right corner. The arrow is captioned "Patient direct reporting of symptoms."

Slide 9

Image: "Patient Experiences Symptom to Research Database and Clinician" is shown. A text box labeled "Patient experiences symptoms" is in the upper-left corner of the slide, and an arrow points down to a data bin labeled "Research database" in the lower right corner. The arrow is captioned "Patient direct reporting of symptoms." A second, uncaptioned arrow points from "Patient experiences symptom" to a text box labeled "Clinician."

Slide 10

Available Technologies

Web-based

Handheld devices

IVRS

Paper

Text messaging

Interviewer

Mixed methods/modes catering to patients

Slide 11

Image: A text box labeled "Patient experiences symptoms" is in the upper-left corner of the slide, and an arrow points down to a data bin labeled "Research database" in the lower right corner. The arrow is captioned, "Patient direct reporting of symptoms." A second, uncaptioned arrow points from "Patient experiences symptom" to a text box labeled "Clinician." A broken line leads from the "Clinician" box to the "Research Data" bin; this line is captioned, "Assign attribution; initiate expedited reporting."

Slide 12

Image: A text box labeled "Patient experiences symptoms" is in the upper-left corner of the slide, and an arrow points down to a data bin labeled "Research database" in the lower right corner. The arrow is captioned, "Patient direct reporting of symptoms." A second, uncaptioned arrow points from "Patient experiences symptom" to a text box labeled "Clinician." There is also a broken-line arrow pointing back from the "Clinician" box to the "Patient experiences symptoms" box. A broken line leads from the "Clinician" box to the "Research Data" bin; this line is captioned, "Assign attribution; initiate expedited reporting."

Slide 14

Patients will broadly endorse symptoms if asked, making it impossible to distinguish AEs between study arms.

Will not be helpful in unmasking serious or unexpected AEs.

Slide 15

Feasibility

High rates of adherence in multi-center industry trials for patient-reported symptoms (IVRS).

Image: A bar chart shows mean and median rates of adherence for different patient populations. Data is provided in the table below:

Population

Mean

Median

Young

79%

83%

Adult

88%

92%

Elderly

91%

96%

Male

87%

93%

Female

87%

92%

<8 Assess

91%

95%

>8 Assess

86%

92%

Fibromyalgia

85%

91%

Osteoarthritis

91%

95%

Post Herpetic Neuralgia

90%

95%

Meacham & Wenzel (Perceptive Informatics/ClinPhone): ISPOR, 2008.

Slide 16

Feasibility

Little attrition over time (Web -based)

Including non-Web avid, elderly, end-stage with high symptom burdens

Image: A bar chart titled "Proportion of Patients Completing Online Questionnaire at a Given Clinic Visit (%)" is shown. The chart is organized by visit numbers 1-24. It ranges from a high of 100% (Visit 1) down to 65% (Visit 15).

Slide 17

Closed after 841/1,125 patients enrolled due to unexpected excess of early deaths in Arm 1 ("IFL")

Associated with "GI syndrome" including severe diarrhea.

Diarrhea reporting:

Clinicians reported toxicities at each cycle (diarrhea required).

Patients reported diarrhea via in HRQL (SDS) every other cycle.

Rothenberg: JCO, 2001.

Slide 18

Clinician-Reported Diarrhea

Image: A line graph shows event-free probability by the Arm cycle. All three Arms begin at 100% event-free probability. Arm 1 drops below 80% in the first cycle and continues to drop to ~60% by cycle 14. Arm 2 (HR = 0.29 vs. Arm 1, P-value <0.001) drops only slightly from cycle to cycle and levels off at ~90% by cycle 7, where it remains until cycle 20 at the end of the chart. Arm 3 (HR = 0.77 vs. Arm 1, P-value <0.05) drops below 80% in the first cycle and continues to drop to 65% by cycle 10; it then remains level until cycle 20.

Dueck: Unpublished Data, 2010.

Slide 19

Patient-Reported Diarrhea

Image: A line graphs shows event-free probability by the Arm cycle. All three Arms begin at 100% event-free probability. Arm 1 drops slightly after the first cycle, then drops sharply to ~65% by cycle 2; thereafter, it drops slightly in each subsequent cycle to end at ~30% in cycle 10. Arm 2 (HR = 0.36 vs. Arm 1, P-value <0.001) drops to ~90% by cycle 3, then below 80% by cycle 4; thereafter, it drops slightly in each subsequent cycle to end just above 50% in cycle 20. Arm 3 (HR = 0.68 vs. Arm 1, P-value <0.003) drops to ~90% in cycle 2, then drops sharply to ~70% by cycle 3; thereafter, it drops slightly in each subsequent cycle to end at ~ 30% in cycle 20.

Dueck: Unpublished Data, 2010.

Slide 20

Patient vs. Clinician Diarrhea in Arm 1 (IFL)

Image: A line chart shows event-free probability for Arm 1 (IFL). Both the Clinician- and Patient-reported lines begin at 100% event-free probability. The Clinician-reported line drops to 80% in the first cycle and continues to drop to ~60% by cycle 14. The Patient-reported line drops slightly after the first cycle, then drops sharply to ~65% by cycle 2; thereafter, it drops slightly in each subsequent cycle to end at ~30% in cycle 10.

Dueck: Unpublished Data, 2010.

Slide 21

Adverse Symptoms Are Common

Many adverse reactions in drug labels are symptoms.

Indication

# of U.S. Approved Drug Labels

Average # of AEs per Label

Total # of Unique AEs across Labels

Proportion of AEs Which are Symptoms

Breast Cancer

32

78

616

36% (223/616)

Asthma

35

54

368

49% (180/368)

GERD

18

115

472

45% (213/472)

Hyperlipidemia

28

82

365

43% (158/365)

Osteoarthritis

39

94

684

41% (278/684)

Slide 22

Docetaxel Drug Label

Image: A Docetaxel Drug Label with probabilities of adverse events circled in red is shown.

Slide 23

NCI Contract HHSN261200800043C

Development of the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE)

Initiated October 2008

Slide 24

Mission

Develop a system for patient electronic self-reporting of adverse symptoms in cancer trials, which is widely accepted and used; generates useful data for investigators, regulators, clinicians and patients; and is compatible with existing adverse event reporting software systems.

Slide 25

PRO-CTCAE Network

Image: A chart depicting the PRO-CTCAE Network is shown. The MSKCC Coordinating Center works with the following components:

Slide 28

National "validation" study underway to evaluate measurement properties of items.

Hay: ASCO 2010.
Dueck: ASCO 2010.

Slide 29

Software Platform

Image: A chart depicting the Software Platform is shown. A patient, clinician, or administrator can use a PC, Tablet PC, or PDA via various interfaces that allow them to perform the following specific tasks:

Patient Interface:

Fill out questionnaires.

Clinician Interface:

Build standard questionnaires.

Manage patients.

Configure rules based notifications and alerts.

Schedule questionnaires.

Report and Analytics Interface (available to clinicians):

Generate patient-level reports.

Generate study-level reports.

Administrator Interface:

Administer security policies.

Provision clinical staff.

Configure system.

The software is built on:

Web 2.0

Rules Engine

Spring Framework

Hibernate

Oracle

PostgreSQL

Slide 30

Image: A screen shot of the Pro-CTCAE Patient Symptom Reporter Web site is shown. The Form Builder page is featured.

Slide 31

Image: A screen shot of the Pro-CTCAE Patient Symptom Reporter Web site is shown. The Schedule Form page is featured.

Slide 32

Image: A screen shot of the Pro-CTCAE Patient Symptom Reporter Web site is shown. A questionnaire page is featured.

Slide 33

Image: A screen shot of the Pro-CTCAE Patient Symptom Reporter Web site is shown. The Symptom summary page is featured.

Slide 34

Image: A screen shot of the Pro-CTCAE Patient Symptom Reporter Web site is shown. An auto-generated E-mail is featured.

Slide 35

Survey

729 Stakeholders in Cooperative Groups

Question

Agree

Neutral

Disagree

Systems to collect PROs in trials should be developed

89%

5%

6%

In trials, adverse events should be reported by patients

88%

8%

4%

Potential Barriers

Agree

Neutral

Disagree

Lack of computers

69%

15%

16%

Limited personnel

57%

18%

25%

Solutions to Overcome Barriers

Agree

Neutral

Disagree

Funding (for personnel, dedicated space, training)

79%

13%

8%

Computers

72%

21%

7%

Bruner et al: ISOQOL, 2010.

Slide 36

Conclusions

Electronic patient-reporting of adverse symptoms in clinical trials is feasible and clinically valuable: